{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os, sys\n",
    "import numpy as np\n",
    "from matplotlib.pyplot import *\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\" Extract final stats from resman's diary file\"\"\"\n",
    "def extract_num(lines0, is_reg=False):\n",
    "\n",
    "    if is_reg:\n",
    "        valid_loss_str     = lines0[-5]\n",
    "        valid_accuracy_str = lines0[-6]\n",
    "        train_loss_str     = lines0[-8]\n",
    "        train_accuracy_str = lines0[-9]\n",
    "        average_time_str   = lines0[-10]        \n",
    "        run_time_str       = lines0[-11]   \n",
    "        \n",
    "    else: \n",
    "        valid_loss_str     = lines0[-6]\n",
    "        valid_accuracy_str = lines0[-7]\n",
    "        train_loss_str     = lines0[-10]\n",
    "        train_accuracy_str = lines0[-11]\n",
    "        average_time_str   = lines0[-12]        \n",
    "        run_time_str       = lines0[-13]\n",
    "\n",
    "    valid_loss     = float(valid_loss_str.split( )[-1])\n",
    "    valid_accuracy = float(valid_accuracy_str.split( )[-1])\n",
    "    train_loss     = float(train_loss_str.split( )[-1])\n",
    "    train_accuracy = float(train_accuracy_str.split( )[-1])\n",
    "    run_time       = float(run_time_str.split( )[-1])\n",
    "    \n",
    "    return valid_loss, valid_accuracy, train_loss, train_accuracy, run_time\n",
    "\n",
    "\"\"\" Extract number of total parameters for each net config from resman's diary file\"\"\"\n",
    "def parse_num_params(lines0):\n",
    "    line_str = ''.join(lines0)\n",
    "    idx = line_str.find(\"Total params\")\n",
    "    param_str = line_str[idx+14:idx+14+20] # 14 is the length of string \"Total params: \"\n",
    "    param_num = param_str.split(\"\\n\")[0]\n",
    "    return int(locale.atof(param_num))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def extract_dim_results(results_dir):\n",
    "    # Dim: subspace dim\n",
    "    # AR: averaged reward (last 100 eps.)\n",
    "    # NE: num of episode to achieve reward 195\n",
    "\n",
    "    # filename list of diary\n",
    "    diary_names = []\n",
    "    for subdir, dirs, files in os.walk(results_dir):\n",
    "        for f in files:\n",
    "            if f == 'diary':\n",
    "                fname = os.path.join(subdir, f)\n",
    "                diary_names.append(fname)\n",
    "\n",
    "    # print diary_names   \n",
    "\n",
    "    dim = []\n",
    "    for f in diary_names:\n",
    "        # print f\n",
    "        tmp_str = f.split('/')[-2]\n",
    "        if tmp_str.split('_')[-2]=='LeNet':\n",
    "            d = int(tmp_str.split('_')[-1])\n",
    "            dim.append(d)\n",
    "\n",
    "    dim = sorted(dim)  \n",
    "\n",
    "    # print dim\n",
    "\n",
    "    diary_names_ordered = []\n",
    "    for d in dim:\n",
    "        for f in diary_names:\n",
    "            if '_'+str(d)+'/' in f:\n",
    "                # print \"%d is in\" % d + f\n",
    "                diary_names_ordered.append(f)           \n",
    "    # print diary_names_ordered\n",
    "    # intrinsic update method\n",
    "    Dim= []\n",
    "    Acc = []\n",
    "\n",
    "    for fname in diary_names_ordered:\n",
    "        tmp_str = fname.split('/')[-2]\n",
    "        d = int(tmp_str.split('_')[-1])\n",
    "        with open(fname,'r') as ff:\n",
    "            lines0 = ff.readlines()\n",
    "\n",
    "            try: \n",
    "                r = extract_num(lines0,False)[1]\n",
    "                # print \"%d dim:\\n\"% d + str(r) + \"\\n\"\n",
    "                Dim.append(d)\n",
    "                Acc.append(r)\n",
    "\n",
    "            except ValueError:\n",
    "                print \"%d dim:\\n\"%d + \"Error \\n\"\n",
    "                pass\n",
    "\n",
    "    return Dim, Acc\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_perf_dim(Dim, Acc, Cx=None):\n",
    "    fig, ax = subplots(figsize=(6,5) )\n",
    "\n",
    "    font = {'size'   : 12}\n",
    "    matplotlib.rc('font', **font)\n",
    "\n",
    "    for i in range(len(Dim)):\n",
    "        if Cx == None:\n",
    "            if Acc[i]>Acc[0]*0.9 and i>0:\n",
    "                print \"d_{int}=\"+str(Dim[i]) + ', acc:' + str(Acc[i])\n",
    "                break\n",
    "        else: \n",
    "            if Acc[i]>Cx and i>0:\n",
    "                print \"d_{int}=\"+str(Dim[i]) + ', acc:' + str(Acc[i])\n",
    "                break\n",
    "                \n",
    "            \n",
    "    \n",
    "    plot(Dim[1:], Acc[1:], 'o', mec='b', mfc=(.8,.8,1), ms=10)\n",
    "    plot(Dim[i], Acc[i], 'o', mec='b', mfc='b', ms=10)\n",
    "    axhline(Acc[0], ls='-', color='k',label='baseline')\n",
    "    axhline(Acc[0] * .9, ls=':', color='k',label='solved')\n",
    "    plt.legend()\n",
    "    ax.set_xlabel('Subspace dimension $d$')\n",
    "    ax.set_ylabel('Validation accuracy')\n",
    "\n",
    "    # ax.set_title('Cifar: Untied_LeNet' )\n",
    "    plt.grid()\n",
    "    ax.set_ylim([0.0,1.01])\n",
    "    # fig.savefig(\"figs/fnn_mnistPL_W\"+str(width[i])+\"_L\"+str(depth[j])+\".pdf\", bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1000, 1500, 1750, 2000, 2250, 2500, 2750, 3000, 3500, 4000, 4500, 5000, 5500, 6000, 6500, 6750, 7000, 7500, 8000, 9000, 10000, 11000, 12000, 13000, 14000, 15000] [0.6112, 0.3731, 0.4075, 0.4165, 0.4303, 0.4334, 0.4465, 0.4503, 0.4571, 0.4649, 0.4745, 0.4825, 0.4913, 0.4851, 0.4971, 0.4959, 0.5025, 0.5041, 0.5095, 0.5125, 0.5208, 0.5301, 0.5351, 0.5404, 0.5406, 0.5406, 0.5508]\n",
      "d_{int}=6750, acc:0.5025\n",
      "17500 dim:\n",
      "Error \n",
      "\n",
      "20000 dim:\n",
      "Error \n",
      "\n",
      "25000 dim:\n",
      "Error \n",
      "\n",
      "[0, 1000, 1500, 1750, 2000, 2250, 2500, 2750, 3000, 3500, 4000, 4500, 5000, 5500, 6000, 6500, 6750, 7000, 7500, 8000, 9000, 10000, 11000, 12000, 13000, 14000, 15000] [0.5231, 0.265, 0.2853, 0.3007, 0.3116, 0.3168, 0.32, 0.3283, 0.335, 0.343, 0.3533, 0.3585, 0.3617, 0.3722, 0.3745, 0.3794, 0.3838, 0.3865, 0.3914, 0.3934, 0.3984, 0.3994, 0.4093, 0.4095, 0.419, 0.4198, 0.4284]\n",
      "d_{int}=600, acc:0.9001\n",
      "[0, 100, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 550, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1750, 2000, 2250, 2500] [0.9758, 0.4056, 0.6268, 0.6375, 0.6996, 0.702, 0.6986, 0.7145, 0.7238, 0.7534, 0.7643, 0.756, 0.7703, 0.7735, 0.7846, 0.7992, 0.8061, 0.82, 0.8437, 0.8557, 0.865, 0.8646, 0.8677, 0.877, 0.8787, 0.887, 0.8892, 0.9004, 0.9051, 0.9108]\n",
      "d_{int}=2000, acc:0.9004\n"
     ]
    },
    {
     "data": {
      "image/png": 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B/wLWmtlvzeyEJIMUEZGGVVADt5lVEMyjPQjoCkwH/gVMM7M76z88EREpBU3iFDKz7wE/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4fe35a1090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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GKSIitSurBm4zKyEYR7sf0AGYAPwLeNLM7qv58EREpBDUi1PIzL4P/AjoA/wV+C3wgrtv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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4fe35a1510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ISAmpMVm4+xR3Px9oT3jO4nxgkZk9bWZ9zaxJMYIUEZH6FXVk2dXu/oC7fxM4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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4fe37639d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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lXZP4ehERKXJRu87+FphpZmOAJYR2haHx9SIiUuSiBosfE+bhvhToBCwnNDyP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\nIiKSkYKFiIhk9P8BJAZYyYAoT/YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4fe3741550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Dim, AR, NE = [],[],[]\n",
    "results_root = '/home/users/chunyuan.li/public_results/chun/'\n",
    "\n",
    "dataset = ['cifar', 'mnist']\n",
    "net     = ['Untied_LeNet', 'MLP_LeNet']\n",
    "\n",
    "\n",
    "exp_folder = 'results_cifar_Untied_LeNet2'\n",
    "results_dir = results_root + exp_folder\n",
    "Dim, Acc= extract_dim_results(results_dir)\n",
    "print Dim, Acc\n",
    "plot_perf_dim(Dim, Acc, 0.5)\n",
    "\n",
    "\n",
    "exp_folder = 'results_cifar_MLP_LeNet3'\n",
    "results_dir = results_root + exp_folder\n",
    "Dim, Acc= extract_dim_results(results_dir)\n",
    "print Dim, Acc\n",
    "plot_perf_dim(Dim, Acc, 0.5)\n",
    "\n",
    "\n",
    "exp_folder = 'results_mnist_Untied_LeNet3'\n",
    "results_dir = results_root + exp_folder\n",
    "Dim, Acc= extract_dim_results(results_dir)\n",
    "# print Dim, Acc\n",
    "plot_perf_dim(Dim, Acc, 0.9)\n",
    "\n",
    "\n",
    "exp_folder = 'results_mnist_MLP_LeNet3'\n",
    "results_dir = results_root + exp_folder\n",
    "Dim, Acc= extract_dim_results(results_dir)\n",
    "print Dim, Acc\n",
    "plot_perf_dim(Dim, Acc, 0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
